Margaret R. Stedman

Primary Medication Non-Adherence: Analysis of 195,930 Electronic Prescriptions

ABSTRACTBACKGROUNDNon-adherence to essential medications represents an important public health problem. Little is known about the frequency with which patients fail to fill prescriptions when new medications are started (“primary non-adherence”) or predictors of failure to fill.OBJECTIVEEvaluate primary non-adherence in community-based practices and identify predictors of non-adherence.PARTICIPANTS75,589 patients treated by 1,217 prescribers in the first year of a community-based e-prescribing initiative.DESIGNWe compiled all e-prescriptions written over a 12-month period and used filled claims to identify filled prescriptions. We calculated primary adherence and non-adherence rates for all e-prescriptions and for new medication starts and compared the rates across patient and medication characteristics. Using multivariable regressions analyses, we examined which characteristics were associated with non-adherence.MAIN MEASURESPrimary medication non-adherence.KEY RESULTSOf 195,930 e-prescriptions, 151,837 (78%) were filled. Of 82,245 e-prescriptions for new medications, 58,984 (72%) were filled. Primary adherence rates were higher for prescriptions written by primary care specialists, especially pediatricians (84%). Patients aged 18 and younger filled prescriptions at the highest rate (87%). In multivariate analyses, medication class was the strongest predictor of adherence, and non-adherence was common for newly prescribed medications treating chronic conditions such as hypertension (28.4%), hyperlipidemia (28.2%), and diabetes (31.4%).CONCLUSIONSMany e-prescriptions were not filled. Previous studies of medication non-adherence failed to capture these prescriptions. Efforts to increase primary adherence could dramatically improve the effectiveness of medication therapy. Interventions that target specific medication classes may be most effective.

Seizure Outcomes Following the Use of Generic versus Brand-Name Antiepileptic Drugs A Systematic Review and Meta-Analysis

AbstractBackground: The automatic substitution of bioequivalent generics for brand-name antiepileptic drugs (AEDs) has been linked by anecdotal reports to loss of seizure control. Objective: To evaluate studies comparing brand-name and generic AEDs, and determine whether evidence exists of superiority of the brand-name version in maintaining seizure control. Data Sources: English-language human studies identified in searches of MEDLINE, EMBASE and International Pharmaceutical Abstracts (1984 to 2009). Study Selection: Randomized controlled trials (RCTs) and observational studies comparing seizure events or seizure-related outcomes between one brand-name AED and at least one alternative version produced by a distinct manufacturer. Data Extraction: We identified 16 articles (9 RCTs, 1 prospective non-randomized trial, 6 observational studies). We assessed characteristics of the studies and, for RCTs, extracted counts for patients whose seizures were characterized as ‘controlled’ and ‘uncontrolled’. Data Synthesis: Seven RCTs were included in the meta-analysis. The aggregate odds ratio (n = 204) was 1.1 (95% CI 0.9, 1.2), indicating no difference in the odds of uncontrolled seizure for patients on generic medications compared with patients on brand-name medications. In contrast, the observational studies identified trends in drug or health services utilization that the authors attributed to changes in seizure control. Conclusions: Although most RCTs were short-term evaluations, the available evidence does not suggest an association between loss of seizure control and generic substitution of at least three types of AEDs. The observational study data may be explained by factors such as undue concern from patients or physicians about the effectiveness of generic AEDs after a recent switch. In the absence of better data, physicians may want to consider more intensive monitoring of high-risk patients taking AEDs when any switch occurs.

Effect of Electronic Prescribing With Formulary Decision Support on Medication Use and Cost

BACKGROUND Electronic prescribing (e-prescribing) with formulary decision support (FDS) prompts prescribers to prescribe lower-cost medications and may help contain health care costs. In April 2004, 2 large Massachusetts insurers began providing an e-prescribing system with FDS to community-based practices. METHODS Using 18 months (October 1, 2003, to March 31, 2005) of administrative data, we conducted a pre-post study with concurrent controls. We first compared the change in the proportion of prescriptions for 3 formulary tiers before and after e-prescribing began, then developed multivariate longitudinal models to estimate the specific effect of e-prescribing when controlling for baseline differences between intervention and control prescribers. Potential savings were estimated using average medication costs by formulary tier. RESULTS More than 1.5 million patients filled 17.4 million prescriptions during the study period. Multivariate models controlling for baseline differences between prescribers and for changes over time estimated that e-prescribing corresponded to a 3.3% increase (95% confidence interval, 2.7%-4.0%) in tier 1 prescribing. The proportion of prescriptions for tiers 2 and 3 (brand-name medications) decreased correspondingly. e-Prescriptions accounted for 20% of filled prescriptions in the intervention group. Based on average costs for private insurers, we estimated that e-prescribing with FDS at this rate could result in savings of

Relative Effectiveness of Osteoporosis Drugs for Preventing Nonvertebral Fracture

845,000 per 100,000 patients. Higher levels of e-prescribing use would increase these savings. CONCLUSIONS Clinicians using e-prescribing with FDS were significantly more likely to prescribe tier 1 medications, and the potential financial savings were substantial. Widespread use of e-prescribing systems with FDS could result in reduced spending on medications.

Comparison of Prospective and Retrospective Indicators of the Quality of End-of-Life Cancer Care

Context Few studies have evaluated the relative effectiveness of drug therapies for osteoporosis. Contribution This study compared nonvertebral fractures that occurred within 1 year of initiating osteoporosis pharmacotherapy among 43135 enrollees in 2 statewide pharmaceutical benefit programs. Differences in fracture risk between adults prescribed risedronate or raloxifene and those prescribed alendronate were small. Fracture risk seemed to be higher with calcitonin than alendronate. Caution Wide confidence bounds around risk estimates did not rule out potentially important differences between some agents. No adherence data were available, and the ability to account for confounders was limited. Implication There probably is no single clearly superior drug therapy for osteoporosis. The Editors Osteoporosis is characterized by decreased bone mass and deterioration of bone tissue, resulting in reduced bone strength and increased fracture risk (1, 2). Approved therapies for osteoporosis include bisphosphonates, calcitonin, raloxifene, and teriparatide. Findings from randomized, controlled, head-to-head trials show that women who received alendronate have greater gains in bone mineral density and greater reductions in bone turnover markers within 12 and 24 months of initiation than those who received risedronate (3, 4) or raloxifene (57). Although bone mineral density is a strong predictor of fracture (8), differences in these surrogate markers may not translate into appreciable differences in fracture risk (911). Results from observational studies suggest that risedronate may reduce the risk for nonvertebral fracture (clavicle, hip, humerus, leg, pelvis, and wrist) within 12 months more effectively than alendronate or nasal calcitonin (12, 13). To our knowledge, no studies have compared the relative effectiveness of raloxifene versus bisphosphonates or calcitonin in reducing fracture risk. Further comparative effectiveness studies may help to clarify the relative effectiveness of osteoporosis treatments (14). We completed a population-based study of new recipients of oral bisphosphonates (alendronate or risedronate), nasal calcitonin, and raloxifene to compare the relative effectiveness of these agents in reducing nonvertebral fracture risk. Methods Study Cohort The study population comprised Medicare beneficiaries enrolled in 2 statewide pharmaceutical benefit plans: the New Jersey Pharmaceutical Assistance to the Aged and Disabled program and the Pennsylvania Pharmaceutical Assistance Contract for the Elderly. These programs provide drug coverage without restriction for low-income residents age 65 years or older with minimal copayment. Our cohort consisted of new recipients (no use of any of these agents in the previous year) of oral bisphosphonates (alendronate, 10 mg or 70 mg, or risedronate, 5 mg or 35 mg), nasal calcitonin, or raloxifene between 1 April 2000 and 30 June 2005 (Figure 1). To ensure complete plan coverage, study eligibility was limited to patients with 1 or more claims in both Medicare and their state pharmaceutical assistance plan in each of the three 6-month intervals preceding the index prescription. We excluded nursing home residents (for whom prescription data may not be complete), patients with a Medicare claim for Paget disease (International Classification of Diseases, Ninth Revision, Clinical Modification code 731.0), and patients with pharmacy claims indicating receipt of any bisphosphonate or teriparatide in the year before treatment initiation. Our data included all Medicare beneficiaries from the 2 plans that met eligibility criteria; we did not do formal sample size calculations. We restricted inclusion to the period when all drugs were available: that is, 1 April 2000 (risedronate received U.S. Food and Drug Administration approval in April 2000). At the time of analysis, we had complete Medicare data from 1 April 2000 to 31 December 2003 for New Jersey and from 1 April 2000 to 31 December 2005 for Pennsylvania. Figure 1. Study flow diagram. Osteoporosis drugs were oral bisphosphonates (alendronate, 10 mg or 70 mg; risedronate, 5 mg or 35 mg), nasal calcitonin, or raloxifene. *May meet >1 exclusion criterion. Outcomes Our primary outcome of interest was nonvertebral fracture within 12 months of treatment initiation. We defined nonvertebral fracture as a fracture of the hip, humerus, or radius or ulna by using previously validated criteria requiring diagnostic and procedural codes from Medicare claims (15). When medical records are used as the reference standard, the estimated sensitivity of each outcome is at least 90% (15). Secondary outcomes included nonvertebral fractures within 6 and 24 months of treatment initiation and hip fracture within 6, 12, and 24 months of treatment initiation. Covariates Patient demographic characteristics were determined at treatment initiation and other variables by medical and pharmacy claims within the year before treatment initiation. We considered covariates that were plausibly related to our fracture outcomes (16): demographic characteristics (age, sex, race), osteoporosis-related factors (such as diagnosis of osteoporosis, fracture history), relevant comorbid conditions (such as comorbidity score [17, 18]; diabetes mellitus; history of falls, syncope, and gait abnormalities; cancer; rheumatoid arthritis), drug use (such as antiepileptics, -blockers, benzodiazepines, glucocorticoids, hormone therapy, selective serotonin reuptake inhibitors, thiazide diuretics, number of drugs), and previous hospitalization. We also included calendar time (month and year) of the index prescription to adjust for potential secular trends in prescribing. Appendix Table 1 lists all variables, definitions, and coding. If a record of a specific diagnosis, procedure, or prescription was lacking, patients were coded as not having these characteristics. As a result of this coding rule, there were no participants for whom exposure, confounder, or outcome information was missing. However, race was unknown in 41 patients. These 41 missing data points were recoded as Caucasian. Appendix Table 1. Definition and Coding of Variables Included in Multinomial Logistic Regression Model Used to Create Propensity Scores Statistical Analysis We calculated fracture rates among recipients of each drug within 6, 12, and 24 months of treatment initiation. We used KaplanMeier methods to plot cumulative fracture incidence and Cox proportional hazard models to compare fracture rates between agents. In our primary analysis, we considered a patient exposed to drug throughout follow-up by censoring only at date of death or end of follow-up (hereafter referred to as intent-to-treat analysis, an analogue of intention-to-treat analysis). We tested proportional hazard assumptions by using interaction terms between exposures and time and found no violations for the primary analysis of 12-month follow-up. However, we observed a violation resulting in an attenuated effect for raloxifene over 24 months. This observation is expected when an intent-to-treat scenario is assumed because adherence to osteoporosis pharmacotherapy is suboptimal (19, 20). Similar attenuation of effects was also observed when hip fracture was the outcome. We developed propensity scores for each drug by using multinomial logistic regression (21). Alendronate, the most commonly prescribed osteoporosis treatment, was selected as the reference category. To account for baseline differences between New Jersey and Pennsylvania, we derived state-specific propensity scores and stratified all adjusted Cox proportional hazard models by state. Propensity score quintiles for risedronate, calcitonin, and raloxifene were included as 12 dummy variables (4 for each drug) to adjust for confounding (2124). We summarized the balance achieved within state-specific propensity score quintiles into descriptive tables and examined the magnitude of difference for each covariate within each propensity score quintile. Preliminary examination suggested residual imbalance within some quintiles (Appendix Tables 2 and 3). For example, within the lowest propensity score quintile for receipt of raloxifene in New Jersey (Appendix Table 2), 76% of patients who received alendronate and 87% of patients who received raloxifene had a background prevalence of osteoporosis. Therefore, we included age groups, fracture history, race, and diagnosis of osteoporosis, in addition to propensity score quintiles, in our adjusted regression models. Analyses were performed with SAS software, version 9.1 (SAS Institute, Cary, North Carolina). Appendix Table 2. Propensity Score Quintiles: New Jersey* Appendix Table 3. Propensity Score Quintiles: Pennsylvania* We used sensitivity analysis to examine the robustness of our findings. First, we examined outcomes assuming an on-treatment scenario. We censored patients on the first day of switching agents, losing drug plan eligibility, entering a nursing home, or discontinuing drug therapy (last date covered by drug plus 15 days, allowing for 30-day gaps between prescriptions), on the day of death, or at the end of follow-up (12 months, 31 December 2003 [New Jersey], or 31 December 2005 [Pennsylvania]). Second, we extended the days that patients received therapy in our on-treatment scenario to the last date covered by drug plus 90 days. Third, we examined several different subgroups: history of any fracture (International Classification of Diseases, Ninth Revision, Clinical Modification codes 733.1x and 800.xx through 829.xx) within the year before treatment initiation, no fracture history, at least 2 consecutive prescriptions of their index drug, no known history of malignant neoplasm, osteoporosis diagnosis, no diagnosis of osteoporosis, and women with no previous hormone therapy. Fourth, given that the main risk factors for fracture measurable in our data set are previous fracture and age, we compared fracture rates stratified by fra

Hospice, opiates, and acute care service use among the elderly before death from heart failure or cancer

PURPOSE To compare prospectively and retrospectively defined benchmarks for the quality of end-of-life care, including a novel indicator for the use of opiate analgesia. METHODS Linked claims and cancer registry data from 1994 to 2003 for New Jersey and Pennsylvania were used to examine prospective and retrospective benchmarks for seniors with breast, colorectal, lung, or prostate cancer who participated in state pharmaceutical benefit programs. RESULTS Use of opiates, particularly long-acting opiates, was low in both the prospective and retrospective cohorts (9.1% and 10.1%, respectively), which supported the underuse of palliative care at the end-of-life. Although hospice was used more commonly in the retrospective versus prospective cohort, admission to hospice within 3 days of death was similar in both cohorts (28.8% v 26.4%), as was the rate of death in an acute care hospital. Retrospective and prospective measures identified similar physician and hospital patterns of end-of-life care. In multivariate models, a visit with an oncologist was positively associated with the use of chemotherapy, opiates, and hospice. Patients who were cared for by oncologists in small group practices were more likely to receive chemotherapy (retrospective only) and less likely to receive hospice (both) than those in large groups. Compared with patients who were cared for in teaching hospitals, those in other hospitals were more likely to receive chemotherapy (both) and to have toxicity (prospective) but were less likely to receive opiates (both) and hospice (retrospective). CONCLUSION Retrospective and prospective measures, including a new measure of the use of opiate analgesia, identify some similar physician and hospital patterns of end-of-life care.

A Systematic Review of Adherence to Cardiovascular Medications in Resource-Limited Settings

BACKGROUND Advances in heart failure (HF) treatments have prolonged survival, but more patients die of HF than of any type of cancer. Little is known about the current practice in end-of-life (EOL) care in HF. METHODS Two EOL cohorts (HF and cancer) were identified using Medicare data linked with pharmacy and cancer registry data. We assessed use of hospice, opiates, and acute care services (hospitalizations, emergency department [ED] visits, intensive care unit [ICU] admissions, and death in acute care). Time trends and predictors of use were assessed using multivariate regression including demographics and cardiovascular and noncardiovasuclar comorbidities. RESULTS Among 5,836 HF patients with median age of 85, 77% female and 4% black, 20% were referred to hospice compared to 51% of 7,565 cancer patients. A modest rise in hospice use over time was parallel in the 2 groups. Twenty-two percent of HF patients filled opiate prescriptions during 60 days before death compared to 46% of cancer patients. Use of acute care services in the 30 days before death was higher for HF (64% vs 39% for ED visits, 60% vs 45% for hospitalizations, and 19% vs 7% for ICU admission). More HF patients died during acute hospitalizations than cancer patients (39% vs 21%). CONCLUSION Patients dying of HF were less likely to be supported by hospice and opiates but more likely to die in hospitals than patients with cancer. Our study suggests that opportunities may exist to improve hospice and opiate use in HF patients.

Design of cluster-randomized trials of quality improvement interventions aimed at medical care providers

BackgroundMedications are a cornerstone of the prevention and management of cardiovascular disease. Long-term medication adherence has been the subject of increasing attention in the developed world but has received little attention in resource-limited settings, where the burden of disease is particularly high and growing rapidly. To evaluate prevalence and predictors of non-adherence to cardiovascular medications in this context, we systematically reviewed the peer-reviewed literature.MethodsWe performed an electronic search of Ovid Medline, Embase and International Pharmaceutical Abstracts from 1966 to August 2010 for studies that measured adherence to cardiovascular medications in the developing world. A DerSimonian-Laird random effects method was used to pool the adherence estimates across studies. Between-study heterogeneity was estimated with an I2 statistic and studies were stratified by disease group and the method by which adherence was assessed. Predictors of non-adherence were also examined.FindingsOur search identified 2,353 abstracts, of which 76 studies met our inclusion criteria. Overall adherence was 57.5% (95% confidence interval [CI] 52.3% to 62.7%; I2 0.98) and was consistent across study subgroups. Studies that assessed adherence with pill counts reported higher levels of adherence (62.1%, 95% CI 49.7% to 73.8%; I2 0.83) than those using self-report (54.6%, 95% CI 47.7% to 61.5%; I2 0.93). Adherence did not vary by geographic region, urban vs. rural settings, or the complexity of a patient’s medication regimen. The most common predictors of poor adherence included poor knowledge, negative perceptions about medication, side effects and high medication costs.InterpretationOur study indicates that adherence to cardiovascular medication in resource-limited countries is sub-optimal and appears very similar to that observed in resource-rich countries. Efforts to improve adherence in resource-limited settings should be a priority given the burden of heart disease in this context, the central role of medications in their management, and the clinical and economic consequences of non-adherence.

Patient, physician, pharmacy, and pharmacy benefit design factors related to generic medication use.

Background:Randomized trials aimed at improving the quality of medical care often randomize the provider. Such trials are frequently embedded in health care systems with available automated records, which can be used to enhance the design of the trial. Methods:We consider how available information from automated records can address each of the following concerns in the design of a trial: whether to randomize individual providers or practices; clustering of outcomes among patients in the same practice and its impact on study size; expected heterogeneity in adherence and the response to the intervention; eligibility criteria and the trade-offs between generalizability and internal validity; and blocking or matching to alleviate covariate imbalance across practices. Results:Investigators can use available information from an automated database to estimate the amount of clustering of patients within providers and practices, and these estimates can inform the decision on whether to randomize at the level of the patient, the provider, or the practice. We illustrate calculation of the anticipated design effect for a proposed cluster-randomized trial and its implications for sample size. With available claims data, investigators can apply focused eligibility criteria to exclude subjects and providers with expected low compliance or lower likelihood of benefit, although possibly at some loss of generalizability. Chance imbalances in covariates are more likely when randomization occurs at the level of the practice than at the level of the patient, so we propose a matching score to limit such imbalances by design. Conclusions:Challenges to compliance, expected small effects, and covariate imbalances are particularly likely in cluster-randomized trials of quality improvement interventions. When such trials are embedded in medical systems with available automated records, use of these data can enhance the design of the trial.

Twelve-Year Risk of Revision After Primary Total Hip Replacement in the U.S. Medicare Population

BACKGROUNDIncreased use of generic medications conserves insurer and patient financial resources and may increase patient adherence.OBJECTIVEThe objective of the study is to evaluate whether physician, patient, pharmacy benefit design, or pharmacy characteristics influence the likelihood that patients will use generic drugsDESIGN, SETTING, AND PARTICIPANTSObservational analysis of 2001–2003 pharmacy claims from a large health plan in the Western United States. We evaluated claims for 5,399 patients who filled a new prescription in at least 1 of 5 classes of chronic medications with generic alternatives. We identified patients initiated on generic drugs and those started on branded medications who switched to generic drugs in the subsequent year. We used generalized estimating equations to perform separate analyses assessing the relationship between independent variables and the probability that patients were initiated on or switched to generic drugs.RESULTSOf the 5,399 new prescriptions filled, 1,262 (23.4%) were generics. Of those initiated on branded medications, 606 (14.9%) switched to a generic drug in the same class in the subsequent year. After regression adjustment, patients residing in high-income zip codes were more likely to initiate treatment with a generic than patients in low-income regions (RR = 1.29; 95% C.I. 1.04–1.60); medical subspecialists (RR = 0.82; 0.69–0.95) and obstetrician/gynecologists (RR = 0.81; 0.69–0.98) were less likely than generalist physicians to initiate generics. Pharmacy benefit design and pharmacy type were not associated with initiation of generic medications. However, patients were over 2.5 times more likely to switch from branded to generic medications if they were enrolled in 3-tier pharmacy plans (95% C.I. 1.12–6.09), and patients who used mail-order pharmacies were 60% more likely to switch to a generic (95% C.I. 1.18–2.30) after initiating treatment with a branded drug.CONCLUSIONSPhysician and patient factors have an important influence on generic drug initiation, with the patients who live in the poorest zip codes paradoxically receiving generic drugs least often. While tiered pharmacy benefit designs and mail-order pharmacies helped steer patients towards generic medications once the first prescription has been filled, they had little effect on initial prescriptions. Providing patients and physicians with information about generic alternatives may reduce costs and lead to more equitable care.